Abstract: Web mining involves activities such as document clustering, community mining etc. to be performed on web. Such tasks need measuring semantic similarity between words. This helps in performing web mining activities easily in many applications. Despite the usefulness of semantic similarity measures in these applications, accurately measuring semantic similarity between two words remains a challenging task. In this paper to find the semantic similarity between two words it makes use of information available on the web and uses methods that make use of page counts and snippets to measure semantic similarity between two words. Various word co-occurrence measures are defined using page counts and then integrate those with lexical patterns extracted from text snippets. To identify the numerous semantic relations that exist between two given words, a pattern extraction and clustering methods are used. The optimal combination of page counts-based co-occurrence measures and lexical pattern clusters is learned using support vector machine used to find semantic similarity between two words. Finally semantic similarity measure what is got is in the range [0, 1], is used to determine semantic similarity between two given words. If two given words are highly similar it is expected to be closer to 1, if two given words are not semantically similar then it is expected to be closer to 0.
Keywords: Natural Language Processing, Semantic Similarity, Support Vector Machine, Text Snippets, Web Mining
References:1.George A. Miller , “WordNet: A Lexical Database for English”.
2.D. Lin. Automatic retreival and clustering of similar words.In Proc. of the 17th COLING, pages 768–774 1998.
3.P. Resnik. Using information content to evaluate semantic similarity in a taxonomy. In Proc. of 14th Internation JoinT Conference on Aritificial Intelligence, 1995.
4.J.J. Jiang and D.W. Conrath. Semantic similarity based on corpus statistics and lexical taxonomy. In Proc. of the International Conference on Research in Computational Linguistics ROCLING1998.
5.D. Lin. An information-theoretic definition of similarity. In Proc. Of the 15th ICML, pages 296–304, 1998.
6.D. Mclean, Y. Li, and Z.A. Bandar, “An Approach for Measuring SemanticSimilarity between Words Using Multiple Information Sources,” July/Aug. 2003.
7.R. Cilibrasi and P. Vitanyi, “The Google Similarity Distance,” IEEE Trans. Knowledge and Data Eng., vol.19, no. 3, pp. 370-383, Mar.2007.
8.G. Miller and W. Charles, “Contextual Correlates of Semantic Similarity,” Language and Cognitive Processes, vol. 6, no. 1, pp. 1-28, 1998.
9.V.Hemalatha and Mrs .K. Sarojini, “semantic similarity approach using rsvm based on personalized search in web search engine”,vol 1,November 2012

31-34

9.

Authors:

Kiran Chhabra, Manali Kshirsagar, A. S. Zadgaonkar

Paper Title:

Effective Congestion Indication for Performance Improvement of Random Early Detection

Abstract: A coming together of the technological networks that connect computers on the internet and the social networks that link humans for millennia has been observed in the past few decades. Even as this has led to the changes in the styles of communication, the media has also remained governed by long standing principles of human social interaction. Web-based collaborations have become vital in today’s business environments. They have paved the way for new type of collaborative system. As collaborative Web-based platforms develop into service oriented architectures (SOA), they promote mixed user enriched services. Due to the availability of various SOA frameworks, Web services emerged as the de facto technology to realize flexible compositions of services. Knowledge-intensive environments clearly demand for provisioning of human expertise along with sharing of computing resources or business data through software-based services. To address the challenges, an adaptive approach allowing humans to provide their expertise through services using SOA standards, such as Web Services Description Language (WSDL) and Simple Object Access Protocol (SOAP) is introduced. The seamless integration of humans in the SOA loop triggers numerous social implications, such as evolving expertise and drifting interests of human service providers.
Keywords:Human Provided Services, Service Avatar, Service Oriented Architecture.
References:1.Schall, Daniel. "Human interactions in mixed systems-architecture, protocols, and algorithms." Unpublished Ph. D. thesis, Vienna University of Technology (2009).
2.Schall, D., Skopik, F., Dustdar, S., "Expert Discovery and Interactions in Mixed Service-Oriented Systems," Services Computing, IEEE Transactions on, vol.5, no.2, pp.233, 245, April-June 2012.
3.Schall, D., Hong-Linh Truong, Dustdar, S., "The Human-Provided Services Framework," E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services, 2008 10th IEEE Conference on , vol., no., pp.149,156, 21-24 July 2008.
4.Florian Skopik, Daniel Schall, Harald Psaier, Schahram Dustdar. Adaptive Provisioning of Human Expertise in Service-oriented Systems SAC’11 Proceedings of the 2011 ACM Symposium on Applied Computing, Pages 1568-1575.
5.Vasilyeva, Ekaterina, et al. "Feedback adaptation in web-based learning systems." International Journal of Continuing Engineering Education and Life Long Learning 17.4 (2007): 337-357.

Analysis of Trip Attraction Characteristics of Commercial Land Use in Medium Sized Towns in Kerala

Abstract: Travel demand forecasting is vital for the design of transportation facilities and services, and also for the future development of a town. The study aims to provide a trip attraction model using multiple regression, that is able to predict the trip attracted to any commercial nodes in the medium sized towns in Kerala. This paper also presents an analysis of trip attraction characteristics of the commercial nodes in medium sized towns of Kerala. Using questionnaire survey, the characteristics of the eight selected commercial nodes from the three medium sized towns Tirur, Perinthalmanna, and Ponnani in Kerala are found out. Socioeconomic surveys are conducted for the selected towns for obtaining the origin-destination data. Based on these surveyed data, the characteristics of the selected nodes are studied and correlation and regression analysis are performed. The study showed that the multiple regression model with the independent variables namely the number of employees and percentage of office in the commercial node with the R2 and Adjusted R2 value of 0.999 and 0.9997 respectively gives the better estimate of trip attraction. This model would be very useful for estimating the trips attracted to a new or existing commercial center in any medium sized towns in Kerala, and thus aid to assess the traffic impact of the commercial center on the geometric design of roadways in the surrounding area.
Keywords: correlation, multicollinearity, multiple regression, trip attraction
References:1.Paquette, R.J., Ashford, N.J., and Wright, P.H., “Transportation Engineering Planning and Design,” John Wiley and Sons, Inc., New York, 1981.
2.AASHTO and FHWA. ,”Quick-Response Urban Travel Estimation Techniques and Transferable Parameters,” Users, Guide, National Research Council, Washington, D.C., 1978.
3.Institute of Transportation Engineers, “Transportation impact analyses for site development: an ITE proposed recommended practice,” Washington, DC, Institute of Transportation Engineers, 2005.
4.Alexis M. FILLONE and Michael Ryan TECSON, “Trip Attraction of Mixed-Use Development in Metropolitan Manila,” Proceedings of the Eastern Asia Society for Transportation Studies, Vol.4, October 2003, pp. 860-868.
5.Budi S. Waloejo, Surjono, and Harnen Sulistio, “The Influence of Trip Attraction on the Road’s Level of Service (LOS) at Traditional Market Land Use,” Journal of Applied Environmental and Biological Sciences, pp. 92-96, February 2012.
6.J. David Innes, Michael C. Ircha, and Daniel A. Badoe, “Factors affecting automobile shopping trip destinations,” Journal of Urban Planning and Development, vol. 116, no. 3, Dec. 1990.
7.SCAG Weekend Travel demand Model, Technical Memorandum no.8 - Weekend Trip Attraction Model, Southern california association of Government, June 30, 2008.
8.Abdul Khalik Al-Taei and Amal M. Taher, “Trip Attraction Development Statistical Model in Dohuk City Residential Area,” Al-Rafidain Engineering, vol.14, no.2, 2006.
9.Clare Yu and Peter Lawrence, “Trip Generation Model Development for Albany,” in 30th Conference of Australian Institutes of Transport Research (CAITR), December 2008.
10.Myriam Baumeler, Anja Simma, and Robert Schlich, “Impact of Spatial Variables on Shopping Trips,” STRC 5th Swiss Transport Research Conference March 9-11, 2005.
11.Barton-Aschman Associates, Inc. and Cambridge Systematics, Inc. “Model Validation and Reasonableness Checking Manual,” February 1997.

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15.

Authors:

Anju Jain, Yogesh Chaba

Paper Title:

Design of Efficient and Reliable MAC protocol for Wireless Technologies

A New Technique to Increase the Working Performance of the Ant Colony Optimization Algorithm

Abstract: The DBSCALE [1] algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. Such meta-heuristic algorithms include Ant Colony Optimization Algorithms, Particle Swarm Optimizations and Genetic Algorithm has received increasing attention in recent years. Ant Colony Optimization (ACO) is a technique that was introduced in the early 1990’s and it is inspired by the foraging behavior of ant colonies.This paper presents an application aiming to cluster a dataset with ACO-based optimization algorithm and to increase the working performance of colony optimization algorithm used for solving data-clustering problem, proposed two new techniques and shows the increase on the performance with the addition of these techniques [5]. We bring out a new clustering initialization algorithm which is scale-invariant to the scale factor. Instead of using the scale factor while the cluster initialization, in this research we determine the number and position of clusters according to the changes of cluster density with the division an agglomeration processes. Experimental results indicate that the proposed DBSCALE has a lower execution time cost than DBSCAN, and IDBSCAN clustering algorithms. IDBSCALE-ACO has a maximum deviation in clustering correctness rate of 95.0% and an error rate of deviation in noise data clustering of 2.62%.This algorithm is proposed to solve combinatorial optimization problem by using Ant Colony algorithm.
Keywords: DBSCALE, Ant Colony Optimization Algorithm, Clustering, Large Datasets.
References:1.M. H. Dunham, Data Mining: Introductory and Advanced Topics, Prentice Hall, 2003
2.A. A. Freitas, S. H. Lavington, Mining very large databases with parallel processing. Dordrecht, The Netherlands, Kluwer Academic Publishers,1998
3.Foster, C. Kesselman, The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, Elsevier Press, 2004, pp. 593-620
4.T. G. Dietterich, An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting andrandomization. Machine Learning Vol.40, 2000, pp.139-158
5.Cheng-Fa Tsai, Chun-Yi Sung, “DBSCALE: An Efficient Density-Based Clustering Algorithm for Data Mining in Large Databases” (PACCS 2010) Second Pacific-Asia Conference on Circuits, Communications and System, 91201 Pingtung, Taiwan, October 2010.
6.R. Agrawal, J. C. Shafer, Parallel mining of association rules IEEE Transactions on Knowledge and Data Engineering, Vol 8., 1996, pp.962-969
7.E. Januzaj, H-P. Kriegel, M. Pfeifle, DBDC: Density-Based Distributed Clustering Proc. 9th Int. Conf. on Extending Database Technology(EDBT), Heraklion, Greece 2004, pp. 88-105
8.N-A. Le-Khac, L. Aouad, and M-T. Kechadi, A new approach for Distributed Density Based Clustering on Grid platform The 24th British National Conference on Databases (BNCOD'07), Springer LNCS 4587, July 3-5, 2007, Glasgow, UK. 2007
9.C. J. Merz, M. J. Pazzani. A principal components approach to combining regression estimates. Machine Learning Vol. 36, 1999, pp. 9-32
10.Kivinen, and H. Mannila, “The power of sampling in knowledgediscovery,” Proceedings of the ACM SIGACT-SIGMOD-SIGART,Minneapolis, Minnesota, United States, May 24 - 27, 1994, pp.77-85
11.K. Sayood, Introduction to Data Compression, 2nd Ed., MorganKaufmann, 2000.

Abstract: We live in an era of computers. From the smallest embedded system to the complex servers that take care of the world economy, we need microprocessors to run them. As time passed by, applications needed more processing power and this lead to an explosive era of research on the architecture of microprocessors. As part of our project we are going to present a technical & comparative study of these smart microprocessors. It will include the different software & hardware technical aspects of such devices for instance OS, applications used, hardware study etc. In this report, we study and compare two microprocessor families that have been at the core of the world’s most popular microprocessors of today – 64 bit microprocessor & Apple microprocessor.
Keywords: CPU, ALU, AMD, RISC, SIMD etc.
References:1.Hadi. “Tablet Ward: Apple iPad vs. BlackBerry Playbook vs. Samsung Galaxy Tab” September 28, 2010
2.Josh Morse “Apple iPad vs. Tablet PC: A comparison” January 28th,2010
3.Apple Developer “Start Developing iPad Apps January 2010
4.Samsung Galaxy Tab Developer Forum "Developing applications for the Samsung Galaxy Tab
5.Techinsights “Apple-iPad- Redefining the table market”
6.Android Operating system a complete perspective
7.Nick Van Elslander, Mario Suppan, Thomas De Roy, Tuan Vu, iPhone Research Paper, MAD intensive programming 2009.
8.Windows 7 Architecture – Wikipedia,
9.iOS Developer Community , iOS – Features
10.Develop for iOS
11.Samsung Galaxy Tab vs. the iPad Compare for yourself
12.HTC Android Tablet appearing in CES 2010
13.HTC rumored to be readying an Android Tablet for Q1 2011

Abstract: Most people who live in rural areas come from the low income families. One reason for this is that the revenue generation is limited to certain domestic economic activities due to poor access to electricity. As a result, it limits the productivity of the people in these particular areas. By using this new pump where sling pump concept is adopted, it is believe that the people in rural areas could have more access to electricity and simultaneously grow the income related activities. This new pump is capable of providing water supply to the domestic agriculture areas. Sling pump is the enhancement of the coil pump where it powered by the water flow. This pump is driven by a propeller which finally resulting the entire pump to rotate the water and turns it into stream. Water and air enter the rear side of the pump and are forced to flow through a coil of plastic tubes while the pump is rotating. Water will be channeled through a hose and into stock tank or reservoir. The pump is applicable for low head and low flow river. From the experiment, this pump is capable to deliver up to 0.5m3 in a minute for stream water with flow rate 5L/s.
Keywords: Low head, low flow, natural energy, pico hydro, water sling pump.
References:1.M. F. Basar, A. Ahmad, N. Hasim and K. Sopian, “Introduction to the Pico Hydropower and the status of implementation in Malaysia,” IEEE Student Conference on Research and Development (SCOReD), pp. 283-288, ISBN: 978-1-4673-0099-5, Cyberjaya, Malaysia, 19-20 December 2011.
2.Martin Anyi, Brian Kirke, and Sam Ali, “Remote Community Electricfication in Sarawak, Malaysia,” Renewable Energy 35 (2010), Volume 35, Issue 7, pp. 1609-1613, July 2010.
3.P. Maher, and N. Smith, “Pico Hydro Village Power : A Practical Manual for Schemes Up to 5 kW in Hilly Areas”, 2nd Edi., Intermediate Technology Publications, May 2001.
4.A.A. Williams, and R. Simpson, “Pico Hydro – Reducing Technical Risks for Rural Electrification,” Renewable Energy 34 (2009), Volume 34, Issue 8, pp. 1986-1991, August 2009.
5.R.K. Sharma, and T.K. Sharma, “A Textbook of Water Power Engineering”, S. Chand & Company Ltd., First Edition, ISBN : 81-219-2230-5, 2003.
6.Dan New, “Intro to Hydropower, Part 1 : Systems Overview,” Home Power 103, p.p 14 – 20, October & November 2004. Available at: http://www.homepower.com
7.Sling Water Pump, http://www.riferam.com/river/, 2012 ‎

ICTs and Supply Chain: The Competitiveness of Small and Medium Scale Enterprises (SMEs)

Abstract: The number of Small and Medium Enterprises (SMEs) operating in the Adum Central Business District (CBD) of Kumasi, Ghana continues to grow at an increasing rate, but still they do not conform to the right standards and appropriate parameters. No matter what business activities they embark on some Information and Communication Technologies (ICTs) can be effectively used to enhance their operations. This paper finds out the adequacy of dissemination of ICTs and its level of deployment in the operations of SMEs of trade businesses in the Adum CBD and establishes the level of awareness of computers and their related technologies among owner-managers. In order to do an in-depth assessment of the situation, the CBD was put into zones A, B, and C and enterprises were selected at random. Interview procedures and administered questionnaires were used to obtain data for analysis. In effect, the study established that though the level of awareness is high, only 23% of these SMEs use computers whilst 49% use mobile phones to support their businesses. Also, 54% of these enterprises do not have access to the Internet. Thus, the exploitation and deployment of ICTs remain a greater challenge to these enterprises. It is recommended that the Ministry of Trade and Industry and other stakeholders organize programmes to enlighten owner-managers on the prospects of using ICTs to gain competitive advantage. In addition, the ICT industry must be revamped and freed from bottlenecks surrounding access to hardware and software.
Keywords: Awareness, Competitiveness, Digital Enterprise ICTs, Owner-Managers, SMEs, Supply Chain.
References:1.J. Tang, “Competition and Innovation Behaviour,” Research Policy, Vol. 35 pp. 68-82, 2006.
2.D. Stokes and N. Wilson, “Small Business Management & Entrepreneurship,” 5th edition, 2006.
3.V. Kotelnikov, “Small and Medium Enterprises and ICT,” United Nations Development Programme – Asia-Pacific Development Information Programme (UNDP-APDIP) and Asian and Pacific Training Centre for Information and Communication Technology for Development (APCICT), 2007.
4.G.S. Kushwaha, “Competitive Advantage through Information and Communication Technology (ICT) Enabled Supply Chain Management Practices,” International Journal of Enterprise Computing and Business Systems, 1:2, p. 3, 2011.
5.DFID Report on Digital Opportunity Task Force (DOT Force) Action Plan, G8 Summit, Geneva, 1992.
6.M. Kuppusamy, M. Raman and G. Lee, “Whose ICT Investment Matters to Economic Growth: Private or Public? The Malaysian Perspective,” The Electronic Journal on Information Systems in Developing Countries, Vol. 37, No. 7, pp. 1-19, 2009.
7.J. Abor and P. Quartey, “Issues in SME Development in Ghana and South Africa,” International Research Journal of Finance and Economics, Vol. 39, pp. 218-228, 2010.
8.K.C. Laudon and J.P. Laudon, “Management Information Systems: Managing the Digital Firm,” 10th edition, Pearson Prentice Hall, New Jersey, 2007.
9.A.L. Popoola, “Development of a Multi-Channel Personal Computer (PC) Based Data Logger,” Journal of Science and Technology, Vol 27. No. 3 pp. 122-130, 2007.
10.A. Papastathopoulos, “Organizational Forms Based on Information & Communication Technologies (ICTs) Adoption,” Research in Business and Economics Journal, Vol. 32, No. 3, pp. 4-6, 2010.
11.R. Ramakrishnan, “Database Management Systems,” WCB/McGraw-Hill Companies, Madison, USA, 1998.
12.P. Marker, M. Kerry, and L. Wallace, “The Significance of Information and Communication Technologies for Reducing Poverty,” DFID: Fuller Davies, 2002.
13.O. Thompson, “Business Intelligence Success, Lessons Learned,” Technology, 2004.
14.H. Shiels, R. McIvor and D. O'Reilly, “Understanding the Implications of ICT Adoption: Insights from SMEs,” Logistics Information Management, Vol. 16, Issue 5, pp.312 – 326, 2003.
15.T.H. Davenport and J.G. Harris, “Automated Decision Making Comes of Age,” MIT Sloan Management Review, Vol. 46, No. 4, pp. 83-89, 2005.
16.K. Sevrani and R. Bahiti “ICT in Small and Medium Enterprises: (Case of Albania),” ICBS, 2008.
17.A. Bryman and E. Bell, “Business Research Methods,” 2nd edition, Oxford University Press Inc., New York, 2007.
18.R.K. Yin, “Case Study Research: Design and Method,” 3rd edition, London, Sage, 2003.
19.R.A. Krueger and M.A. Casey, “Focus Groups: A Practical Guide for Applied Research,” 3rd edition, Thousand Oaks, CA, Sage, 2000.
20.M. Hall, “Decision Support Systems,” Computerworld, Vol. 36, No. 27, 2002.
21.J. Hoffer, M. Prescott, and F. McFadden, “Modern Database Management,” 6th edition, Upper Saddle River, Prentice Hall, New Jersey, 2002.

203-209

43.

Authors:

Hemant Sharma, Jasvir Singh

Paper Title:

Run off River Plant: Status and Prospects

Abstract: Most of the small hydro power plants are based on Run of River scheme, implying that they do not have any water storage capability. The power is generated only when enough water is available from the river. When the stream flow reduces below the design flow value, the generation will reduce as the water does not flow through the intake structure into the turbine. Small hydro plants may be stand alone system in isolated areas but could also be grid connected. The connection to the grid has the advantage of the easier control of the electrical system frequency of the electricity. In this research paper i discussed about the run off river plant, comparison of runoff river plant and small hydro power plants. And what type of turbine is suitable for small hydro power plant and run off river plant.
Keywords: hydropower, runoff river power plant, water power.
References:1.Jiaqi Liang, Ronald G. Harley “Pumped storage hydro-plant models for system transient and long-term dynamic studies” IEEE 2009.
2.Mukhtiar Singh, and Ambrish Chandra “Modelling and control of isolated micro-hydro power plant with battery storage system” IEEE 2012.
3.H.K. Verma and Arun Kumar “Performance testing of small hydropower plant”International Conference on Small Hydro power 2007.
4.Oliver Paish, “Small Hydro Power- Technology and Current Status: Elseiver Journal Renewable and Sustainable Energy Reviews.
5.Vineesh V, A. Immanuel Selvakumar “ Design of micro hydel power plant” IJEAT 2012.
6.Pankaj kapoor, Lobzang Phunchok, Sunandan Kumar “Frequency control of micro hydro power plant using electronic load controller” IJERA 2012.
7.Viktor Iliev, Predrag Popovski, Zoran Markov “Transient phenomena analysis in hydroelectric power plants at off-design operating conditions” IJERA 2012.
8.Arun Varughese, Prawin Angel Michael “Electrical characteristics of micro-hydro power plant proposed in valara waterfall” IJITEE 2013.
9.Okonkwo, G N, Ezeonu S O “Design and Installation of Mini Hydro Electric Power Plant”, Scholar Journal of Engineering Research April 2012.